Cover Page The handle http://hdl.handle.net/1887/87271

This algorithm suggests to maximize the multiplication of the expected improvement of the objective function and the probability of feasibility, which are both statistical

Although a category (c) transformation provides the possibility for the SOCU optimizer to explore the area close to the feasible subspace with a constant feasibility margin ,

Kriging has the big advantage of providing uncertainty information for surrogates, which is necessary for determining EI. But Kriging – at least in most currently

What we show here for RBF surrogate models holds the same way for GP (or Kriging) surrogate models often used in EGO [90]: Problems with a high condition number have a much

However, the fact that in most cases real-world COPs have objective and constraint functions of different types and nature, motivated us to develop an online model selection

The black point shows the global optimum of the fitness function which is different from the optimum of the constrained problem shown as the gold star.. where n is the size of

SACOBRA, standing for self-adjusting constrained optimization by radial basis function interpolation, is an efficient technique using RBF interpolations as surro- gates for

Deze eigenschap maakt Kriging tot een populaire keuze voor veel optimalisatie algoritmen, vooral efficiente methoden (zonder ondersteuning voor beperkingen) zoals SOCU